Driver state monitors are intended to track the movement of the eye and determine if the driver is becoming distracted or drowsy. In the motor vehicle environment for example, a camera can be used to generate an image of the driver's face and portions of the image can be analyzed to assess a driver's gaze or drowsiness. If such a condition exists, the system can detect the condition and provide an appropriate signal that can set off an alarm to either awaken the driver or alert the driver.
Eye position tracking in two dimensional grey scale image sequences must be at small time intervals to be useful for monitoring purposes. The eye position tracking must also be precise in tracking the eye center in two dimensional images and avoid drift during position tracking due to light changes in outdoor conditions. Other obstacles may also cause the monitoring to be ineffective. Major head motion such as turning the head, closure of the eye lid such as blinking, blink frequency, existence of glasses and sunglasses may all be conditions that may cause a monitoring system to fail by mistracking the eye position.
One of the most challenging problems in computer vision remains the eye position tracking problem. Image processing, computer vision and pattern recognition tools like mathematical morphological operators, contour extraction, interest point detector, eigen analysis and Kalman filter have been utilized in target tracking in visible and infrared imagery. In eye position tracking, it is common to extract the iris area and track it in successive frames with contour-based methods like active contour. Methods like bottom-hat morphological operators are usually employed in extracting the dark facial features in a face image. When dark feature like iris-eye and eyebrow are merged together into one big dark area due to shadow then the bottom-up morphological operator is not helpful. Another way being introduced to work around this issue is inverse receptive hole extractor (IRHE). Tracking methods like Kalman filter and correlation based techniques have some limitations when tracking the eye position in closed and open states as well for any facial feature complexity (dark-features vs. light-features, with and without glasses, with and without eyebrow, eye makeup, etc.). They tend to lose tracking of the eye or track the eye neighbors when the eye goes from open to closed. In particular, computer vision analysis systems such as Receptive Extraction or Inverse Receptive Hole Extraction (IRHE) techniques along with IRIS Extraction programs and Appearance-based Approach techniques have greatly improved tracking. These IRHE advances are disclosed in U.S. Ser. No. 11/300,621 filed on Dec. 14, 2005 by Riad Hammoud and entitled “Method of Locating a Human Eye in a Video Image” and are incorporated herein by reference.
Other methods as disclosed in U.S. Ser. No. 11/150,684 filed on Jun. 10, 2005 by Riad Hammound and Andrew Wilhelm and entitled “System and Method of Detecting an Eye” and U.S. Ser. No. 10/828,005 filed on Apr. 10, 2004 by Riad Hammoud and entitled “Object Tracking and Eye State Identification Method” are also incorporated herein by reference. However, even with other systems such as Single Template Correlation and Four Moving Parts Systems, these methods performed poorly in the tracking subjects with cluttered eye region of first order (for example, eyebrow or eyeglasses being present). Tracking subjects with a cluttered eye region, that is, an eye region where eyeglasses or eyebrows are in close proximity may result in tracking the eyebrow or the eyeglasses rather than the eye which can result in errors in measuring driver state such as driver fatigue or driver distraction.
What is needed is a dynamic improved eye position tracking system which takes into account located and tracked neighbors such as eyebrows or eyeglass frames to determine a constrained search window in which the eye must be located which improves the eye position tracking.